Although as many as 80% of patients who suffer from migraine headaches implicate “weather” as a trigger for migraine episodes, the evidence based on clinical experience and epidemiologic studies is mixed. Not all patients are equally as perceptive of weather parameters and not all patients report the same weather factors as a trigger. Researchers have attempted for years to more clearly delineate weather's role in predicting migraine headache onset, typically evaluating individual meteorologic “weather” factors (e.g., barometric pressure, temperature, humidity, wind) as heterogeneous, continuous predictors of migraine. To date, none have developed a model with a high degree of predictive value, and some researchers have disparaged the concept due to apparent inconsistency in the data.
For example, one study reported that low barometric pressure during the preceding two days was associated with a higher frequency of emergency room visits for severe headache while others found the opposite or no relationship. Temperature has also shown mixed results; high temperature in the preceding 24 hours has been found to increase emergency room visits for severe headache by 7.5% for every 5 degree increase in temperature, but other studies have found no effect of temperature on migraine onset or severity.
Self-help internet sites (e.g., accuweather.com, weather.com) produce daily “migraine” risk models. However, the methodological development is not apparently published or corroborated by any reports of predictive accuracy. These models tend to collapse migraine risk across regions of the country, which could be problematic as one cannot assume the transportability of models from one location to another. The challenge of this and other models is that they can potentially do more harm than good. When the predicted high-risk event fails to materialize (as is often the case), patients become immune to future risk warnings and are thus unprepared when an actual event occurs. Alternatively, lumping large areas of the country under a similar risk level makes the individual less likely to personalize the information.
Another key problem with existing models is lack of scalability. Models generated from population data across varied climate regions, models generated from population data across a single climate region, and models generated from individual data in a single geographic area are not equivalent predictors at the level of an individual.
A migraine early warning system would afford patients an invaluable tool to help them take steps to ward off and/or be prepared to combat a potential attack. Current models that tout a “migraine risk” predictor have limited specificity and, as such, limited utility given their high false positive predictive rates.
Although migraine may be seen as the prototypical episodic disease event given its propensity for being susceptible to changing weather conditions, other diseases and health conditions also exhibit weather dependency. For example, asthma, emphysema (COPD), depression, anxiety, osteoarthritis, fibromyalgia and stroke are all reported in the literature as having a weather-based component to onset, flare-ups and severity.
Conceptual and methodological barriers have impeded efforts to construct a reliable model for prediction of weather-based risk. First, most researchers and patients have considered weather parameters as independent factors rather than evaluating them concurrently and consecutively. Second, most have failed to consider how meteorological changes may be especially predictive of weather-triggered health events. Third, many have only considered linear-based risk. Fourth, researchers often fail to fully account for seasonal weather variations within and across different climate regions. Finally, few have linked reoccurring weather patterns to migraine risk. Clearly, there remains a need in the art for valid, reliable models for predicting risk of weather-associated diseases and medical conditions both for populations and at the level of the individual.